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Updated: Oct 3, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Identification of Immune Subtypes of Lung Squamous Cell Carcinoma by Integrative Genome-Scale Analysis
Liyuan Yin1, Wen Zhang2, Dan Pu1
1Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
Background:
Characterization of the tumor microenvironment is helpful to understand the tumor immune environment of lung cancer and help predict the prognosis.
Methods:
First, immune subtypes were identified by consensus subtype among lung squamous carcinoma (LUSC) patients. Immune cell infiltration was evaluated by CIBERSORT and ESTIMATE analyses. Then, based on differentially expressed genes (DEGs) identified, a risk score model was constructed. Finally, gene FPR1 was validated by using YTMLC-90.
Findings:
LUSC samples were divided into four heterogeneous immune subtypes, with significantly different prognoses with subtype 4 having the poorest overall survival (OS). The immune infiltration score showed that subtype 4 was characterized as immune enriched and fibrotic, while subtype 3 was tumor enriched. DEG analysis showed that upregulated genes in subtype 4 were enriched of neutrophil and exhausted T cell-related biological processes. Based on a univariate Cox regression model, prognostic 7 immune-related genes were combined to construct a risk score model and able to predict OS rates in the validation datasets. Wound healing and transwell assay were conducted to evaluate the invasion property after activating the gene FPR1.
Interpretation:
The analysis of tumor immune microenvironments among LUSC subtypes may provide new insights into the strategy of immunotherapy.
